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فهرست محتوای دیجیتالی
فهرست مطالب
فهرست نقدها
3 مرتبه مشاهده شده
Artificial Intelligence for Customer Relationship Management: Solving Customer Problems
Galitsky, Boris
- ISBN:9783030616403
- Main Entry: Galitsky, Boris
- Title:Artificial Intelligence for Customer Relationship Management: Solving Customer Problems/ Boris Galitsky.-
- Publisher: Cham, Switzerland: Springer, 2021.
فهرست محتوای دیجیتالی-
محتواي کتاب - مشاهده
فهرست مطالب- Preface to Volume 2
- Acknowledgements
- Contents
- 1 Chatbots for CRM and Dialogue Management
- 1.1 Introduction: Maintaining Cohesive Session Flow
- 1.2 Chatbot Architectures and Dialogue Manager
- 1.3 Building Dialogue Structure from a Discourse Tree of an Initial Question
- 1.3.1 Setting a Dialogue Style and Structure by a Query
- 1.3.2 Building a Dialogue Structure in Customer Support Dialogues
- 1.3.3 Finding a Sequence of Answers to be in Agreement with a Question
- 1.3.4 Searching for Answers with Specified RR for Dialogue Construction
- 1.3.5 Datasets for Evaluation
- 1.3.6 Evaluation of the Dialogue Construction from the First Query
- 1.4 Dialogue Management Based on Real and Imaginary Discourse Trees
- 1.5 Dialogue Management Based on Lattice Walking
- 1.6 Automated Building a Dialogue from an Arbitrary Document
- 1.7 Open Source Implementation
- 1.8 Related Work
- 1.9 Conclusions
- References
- 2 Recommendation by Joining a Human Conversation
- 2.1 Introduction
- 2.2 Slot-Filling Conversational Recommendation Systems
- 2.3 Computing Recommendation for a Dialogue
- 2.4 Assuring the Recommendation is Persuasive and Properly Argued For
- 2.5 Continuing Conversation with RJC Agent
- 2.6 System Architecture
- 2.7 Evaluation
- 2.8 Related Work and Discussion
- References
- 3 Adjusting Chatbot Conversation to User Personality and Mood
- 4 A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination
- 5 Concluding a CRM Session
- 6 Truth, Lie and Hypocrisy
- 6.1 Anatomy of a Lie
- 6.1.1 Introduction: A Discourse of a Lie
- 6.1.2 Example of Misrepresentation in User-Generated Content
- 6.1.3 Example of Misrepresentation in Professional Writing
- 6.1.4 Background and Related Work
- 6.1.5 Dataset Description
- 6.1.6 Communicative Discourse Trees to Represent Truthfulness in Text
- 6.1.7 Evaluation
- 6.1.8 Two Dimensions of Lie Detection
- 6.1.9 Fact-Checking Tools
- 6.1.10 Conclusions
- 6.2 Detecting Hypocrisy in Company and Customer Communication
- 6.2.1 Introducing Hypocrisy
- 6.2.2 Hypocrisy in Customer Complaints
- 6.2.3 Building a Dataset of Sentences with Hypocrisy
- 6.2.4 Templates for Sentences with Hypocrisy
- 6.2.5 Assessing Coordination of Prominent Entities
- 6.2.6 Hypocrisy in Tweets
- 6.2.7 Expressing Hypocrisy in a Dialogue
- 6.2.8 System Architecture
- 6.2.9 Evaluation
- 6.2.10 Related Work and Discussions
- 6.2.11 Hypocrysy versus Controversy Stance, Sarcasm, Sentiments
- 6.2.12 Measuring Contention Between Say and Do Parts
- 6.2.13 Hypocrisy and Opinion Formation
- 6.2.14 Conclusions
- 6.3 Detecting Rumor and Disinformation by Web Mining
- References
- 6.1 Anatomy of a Lie
- 7 Reasoning for Resolving Customer Complaints
- 8 Concept-Based Learning of Complainants’ Behavior
- 8.1 Introduction
- 8.2 Logical Simulation of the Behavior
- 8.3 Complaint Validity, Complaint Management and CRM
- 8.4 Complaint Scenario and Communicative Actions
- 8.5 Formalizing Conflict Scenarios
- 8.6 Semantics of Communicative Actions
- 8.7 Defining Scenarios as Graphs and Learning Them
- 8.8 Assigning a Scenario to a Class
- 8.9 JSM Learning in Terms of Formal Concept Analysis
- 8.10 Finding Similarity Between Scenarios
- 8.11 Scenarios as Sequences of Local Logics
- 8.12 Evaluation
- 8.13 Assessing Validity of Travelers’ Complaints
- 8.14 Using ComplaintEngine
- 8.15 Selecting Products by Features Using Customer Feedback
- 8.16 Discussion and Conclusions
- References
- 9 Reasoning and Simulation of Mental Attitudes of a Customer
- 9.1 Introduction
- 9.2 A Model of a Mental Attitude of a Customer
- 9.3 Simulating Reasoning About the Mental States
- 9.4 Implementation of Simulation
- 9.5 Evaluation of the ToM Engine
- 9.6 Introduction to Meta-Reasoning and Introspection of ToM Engine
- 9.7 ToM Engine Support for Customer Complaint Processing
- 9.8 Front End of ToM Engine
- 9.9 Discussion and Conclusions
- References
- 10 CRM Becomes Seriously Ill
- 10.1 Introduction
- 10.2 Defining DI
- 10.3 Companies Sick with Distributed Incompetence
- 10.3.1 Managing Distributed Incompetence Organizations
- 10.3.2 Whistleblowing in Distributed Incompetence Organizations
- 10.3.3 The Financial Crisis and Distributed Incompetence Organizations
- 10.3.4 Distributed Incompetence and Competitive Rating
- 10.3.5 Irrationality of Agents Under Distributed Incompetence
- 10.3.6 Aggressive DI
- 10.3.7 Machine Learning of DI
- 10.4 Detecting DI in Text
- 10.5 Customer Service and Covid-19
- 10.6 Conclusions: Curing Distributed Incompetence
- References
- 11 Conclusions